Definition
An AI agent is a software system that uses a large language model as its reasoning engine to autonomously plan and execute multi-step tasks. Unlike a standard LLM chat interface that produces a single response per query, an agent operates in a loop: it receives a goal, reasons about what steps are needed, selects and calls tools (APIs, databases, code execution), evaluates the results, and decides whether to continue or conclude. The LLM provides the reasoning; the tools provide the ability to act on the real world.
The architecture typically follows a pattern: the LLM receives a prompt containing the user's goal, a description of available tools, and the results of any previous actions. It decides which tool to call next (or whether to respond to the user). The tool executes and returns results. The LLM processes those results and decides the next step. This loop continues until the task is complete. Function calling is the mechanism most models use to interact with tools in a structured way.
AI agents represent a shift from AI as an assistant (answering questions) to AI as a worker (completing tasks). Products like Devin (software engineering agent), Harvey (legal agent), and Claude Code demonstrate this pattern. The agentic AI paradigm is reshaping how PMs think about product design, moving from "features the user operates" to "outcomes the agent delivers." You can evaluate your product's readiness for agentic capabilities with the AI Readiness Assessment.
Why It Matters for Product Managers
AI agents change the product model from tool to delegate. Instead of building interfaces where users click buttons and fill forms, PMs design systems where users state goals and agents execute them. This requires a fundamentally different approach to UX: designing for trust, transparency, error recovery, and appropriate levels of autonomy.
The PM challenge with agents is scoping autonomy correctly. Too little autonomy and the agent is just a chatbot with extra steps. Too much autonomy and users lose trust when the agent takes unexpected actions. The best agent products start with a narrow, high-value task (scheduling a meeting, filing a bug report, generating a weekly report) and expand scope based on demonstrated reliability.
How to Apply It
Start with a single workflow that is repetitive, well-defined, and low-risk. Build the agent for that workflow, instrument it heavily, and expand from there.
Steps for building agent-powered features:
- ☐ Identify repetitive workflows where users follow a predictable sequence of tool interactions
- ☐ Define the tool set: which APIs, databases, and actions the agent can access
- ☐ Implement guardrails: rate limits, human approval gates for irreversible actions, sandboxed execution
- ☐ Build an audit log that captures every reasoning step and tool call for debugging and compliance
- ☐ Design the UX to show the agent's plan and progress, not just the final result
- ☐ Measure task completion rate, error rate, and user override rate as core agent quality metrics
- ☐ Start with human-in-the-loop for all actions and gradually remove gates as confidence grows